Matthew Moffatt

I’m a Senior Data Engineer who thrives working independently on hard problems—designing systems from first principles, picking up new technologies as needed, and taking ownership from architecture through delivery. I focus on writing clean, well-documented code and building data solutions that are as easy to maintain as they are to use.

At Interworks, I built an internal Terraform framework automating our Snowflake Rapid Start service, cutting deployment time from 2–3 weeks to less than 10 minutes and increasing fixed-cost engagement margins by over 200%. I also created an open-source Python data ingestion framework with orchestration-agnostic wrappers (Airflow, Dagster, Prefect), backed by a CI pipeline (pytest, semantic versioning, Docker publishing), replacing tools like Fivetran and Airbyte at an estimated 80–95% lower cost. In parallel, I redesigned a full-funnel marketing analytics data model in AWS Redshift using dbt, medallion architecture, and star schema to power C-Suite Tableau dashboards.

I further architected Terraform-based infrastructure across Snowflake, Fivetran, dbt, Monte Carlo, and AWS—building an isolated dev/CI environment with full prod parity to satisfy strict InfoSec requirements, and a governed Snowflake environment enabling an MLOps team to train and deploy models on curated data. Previously, at Airgas, I used SQL, Python, Power BI, and Azure to improve pipeline latency and query performance by 50%+, create documentation/governance standards, and help deliver $20MM in increased annual sales with a 30% reduction in pricing decision implementation time.